Past performance is no guide to future returns: Why we can't accurately forecast the future
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This document discusses the inability to accurately forecast future energy technology costs and performance over long time horizons. It provides several examples where past energy forecasts made by models and experts were wildly inaccurate when compared to actual outcomes. This is because economic, social and technological systems lack the structural stability and predictability of physical systems. Forecasts are strongly influenced by unexpected events and changes in market structure, technology innovation, policies and other hard-to-predict factors. Therefore, no amount of additional resources or effort can overcome the fundamental limitations of long-term energy forecasting. Experimental approaches and focusing on physical constraints provide better guidance than precise cost predictions decades into the future.
![Some Key References
• Craig, Paul, Ashok Gadgil, and Jonathan Koomey. 2002. "What Can History Teach Us?: A
Retrospective Analysis of Long-term Energy Forecasts for the U.S." In Annual Review of Energy and
the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual
Reviews, Inc. pp. 83-118.
• Ghanadan, Rebecca, and Jonathan Koomey. 2005. "Using Energy Scenarios to Explore Alternative
Energy Pathways in California." Energy Policy. vol. 33, no. 9. June. pp. 1117-1142.
• Hirsh, Richard F., and Jonathan G. Koomey. 2015. "Electricity Consumption and Economic Growth:
A New Relationship with Significant Consequences?" The Electricity Journal. vol. 28, no. 9.
November. pp. 72-84. [http://www.sciencedirect.com/science/article/pii/S1040619015002067]
• Koomey, Jonathan. 2008. Turning Numbers into Knowledge: Mastering the Art of Problem Solving.
Oakland, CA: Analytics Press. 2nd edition. <http://www.analyticspress.com>
• Koomey, Jonathan. 2002. "From My Perspective: Avoiding "The Big Mistake" in Forecasting
Technology Adoption." Technological Forecasting and Social Change. vol. 69, no. 5. June. pp.
511-518.
• Koomey, Jonathan G., Paul Craig, Ashok Gadgil, and David Lorenzetti. 2003. "Improving long-range
energy modeling: A plea for historical retrospectives." The Energy Journal (also LBNL-52448). vol.
24, no. 4. October. pp. 75-92.
• Chapter 4: “Why we can’t accurately forecast the future”, in Koomey, Jonathan G. 2012. Cold Cash,
Cool Climate: Science-Based Advice for Ecological Entrepreneurs. Burlingame, CA: Analytics
Press. [http://www.analyticspress.com/cccc.html]
• Koomey, Jonathan. 2013. "Moving Beyond Benefit-Cost Analysis of Climate Change."
Environmental Research Letters. vol. 8, no. 041005. December 2. [http://iopscience.iop.org/
1748-9326/8/4/041005/]
• Laitner, J.A., S.J. DeCanio, J.G. Koomey, A.H. Sanstad. (2003) “Room for Improvement: Increasing
the Value of Energy Modeling for Policy Analysis.” Utilities Policy, vol. 11, no. 2. June. pp. 87-94.
• Scher, Irene, and Jonathan G. Koomey. 2011. "Is Accurate Forecasting of Economic Systems
Possible?" Climatic Change. Vol 104, No. 3-4, pp.473-479.
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Copyright
Jonathan
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Koomey
2016](https://image.slidesharecdn.com/pastperformancenoguidetofuturereturns-v3-160518192251/85/Past-performance-is-no-guide-to-future-returns-Why-we-can-t-accurately-forecast-the-future-33-320.jpg)
Past performance is no guide to future returns: Why we can't accurately forecast the future
- 1. Past performance is no guide to future returns: Why we can’t accurately forecast the future Jonathan Koomey, Ph.D. Research Fellow, Stanford University http://www.koomey.com Presented on a webinar for US EPA and US DOE May 18, 2016 1 Copyright Jonathan G. Koomey 2016
- 2. My background • Founded LBNL’s End-Use Forecasting group and led that group for more than 11 years. • Peer reviewed articles and books on – Forecasting methodology – Economics of greenhouse gas mitigation – Critical thinking skills – Information technology and resource use 2 Copyright Jonathan G. Koomey 2016
- 3. 3 Cost-benefit analysis: the standard approach Copyright Jonathan G. Koomey 2016
- 4. True or False?: If only we had enough… • Time • Money • Graduate Students • Coffee we could accurately predict the cost of energy technologies in 2050 4 Copyright Jonathan G. Koomey 2016
- 5. Widespread modeling practice implies that the answer is “True” 5 Copyright Jonathan G. Koomey 2016
- 6. Based on my experience and reviews of historical retrospectives on forecasting, I say “No way” 6 Copyright Jonathan G. Koomey 2016
- 7. Aside: Many of the best modelers acknowledge the difficulties in the pursuit of accurate forecasts, but in their heart of hearts they still believe they can predict accurately with greater effort 7 Copyright Jonathan G. Koomey 2016
- 8. Uncertainty affects even physical systems Es=mates of Planck’s constant "h" over =me. In this physical system researchers repeatedly underes=mated the error in their determina=ons. At each stage uncertain=es existed of which the researchers were unaware. The problem of error es=ma=on is far greater in long-‐range energy forecas=ng. Taken from Koomey et al. 2003. 8 Copyright Jonathan G. Koomey 2016
- 9. Forecasting Accuracy: The Models Have Done Badly • Energy forecasting models have little or no ability to accurately predict future energy prices and demand (Craig et al. 2002) • Even the sign of the impacts of proposed policies is a function of key assumptions (Repetto and Austin 1997) • The dismal accuracy and inherent limitations of these models should make modelers modest in the conclusions they draw (Decanio 2003) Craig, P., A. Gadgil, and J. Koomey (2002). “What Can History Teach Us? A Retrospective Analysis of Long-term Energy Forecasts for the U.S.” Annual Review of Energy and the Environment 2002. R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA, Annual Reviews, Inc. (also LBNL-50498). 27: 83-118. Repetto, R. and D. Austin (1997). The Costs of Climate Protection: A Guide for the Perplexed. Washington, DC, World Resources Institute. DeCanio, S. J. (2003). Economic Models of Climate Change: A Critique. Basingstoke, UK, Palgrave-Macmillan. 9 Copyright Jonathan G. Koomey 2016
- 10. One example: 1970s projections of year 2000 U.S. primary energy Source: Craig, Paul, Ashok Gadgil, and Jonathan Koomey. 2002. "What Can History Teach Us?: A Retrospective Analysis of Long-term Energy Forecasts for the U.S." In Annual Review of Energy and the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual Reviews, Inc. (also LBNL-50498). pp. 83-118. 10 Copyright Jonathan G. Koomey 2016
- 11. What drove errors in US primary energy forecasts? Copyright Jonathan G. Koomey 2016 11 Graph from Hirsh and Koomey 2015
- 12. Another example: Oil price projec3ons by U.S. DOE, AEO 1982 through AEO 2000 12 Copyright Jonathan G. Koomey 2016
- 13. Not any beEer aFer 2000: Oil price projec3ons by U.S. DOE, AEO 2000 through AEO 2007 13 Copyright Jonathan G. Koomey 2016
- 14. Yet another example: NERC fan Copyright Jonathan G. Koomey 2016 14 US electricity genera=on BkWh/year
- 15. Why Are Long-term Energy Forecasts Almost Always Wrong? • Core data and assumptions, which drive results, are based on historical experience, which can be misleading if structural conditions change • The exact timing and character of pivotal events and technology changes cannot be predicted Laitner, J.A., S.J. DeCanio, J.G. Koomey, A.H. Sanstad. (2003) “Room for Improvement: Increasing the Value of Energy Modeling for Policy Analysis.” Utilities Policy, 11, pp. 87-94. 15 Copyright Jonathan G. Koomey 2016
- 16. Conditions for Model Accuracy • Hodges and Dewar: models can be accurate when they describe systems that – are observable and permit collection of ample and accurate data – exhibit constancy of structure over time – exhibit constancy across variations in conditions not specified in the model Source: Hodges, James S., and James A. Dewar. 1992. Is it you or your model talking? A framework for model validation. Santa Monica, CA: RAND. ISBN 0-8330-1223-1. 16 Copyright Jonathan G. Koomey 2016
- 17. ∑: Accurate forecasts require structural constancy and no surprises 17 Copyright Jonathan G. Koomey 2016
- 18. Market structure can change fast Source: Scher and Koomey 2010. 18 Copyright Jonathan G. Koomey 2016
- 19. Fast changing markets #2: US electricity consumption Copyright Jonathan G. Koomey 2016 19 Graph from Hirsh and Koomey 2015
- 20. Surprises can be big: U.S. nuclear busbar costs Source: Koomey and Hultman 2007. Assumes 7% real discount rate. Projected cost range from Tybout 1957 20 Copyright Jonathan G. Koomey 2016
- 21. Implications for long-term energy forecasting • Forecasting models describing well-defined physical systems using correct parameters can be accurate because physical laws are geographically and temporally invariant (as long as there are no surprises) • Economic, social, and technological systems do not exhibit the required structural constancy, so models forecasting the future of these systems are doomed to be inaccurate. Four big sources of inconstancy – Pivotal events (like Sept. 11th or the 1970s oil shocks) – Technological innovation – Institutional change – Policy choices 21 Copyright Jonathan G. Koomey 2016
- 22. ∑: Economics ≠ Physics 22 Copyright Jonathan G. Koomey 2016
- 23. So no matter how many $, coffee cups, months, or graduate students you have, accurate long- run forecasting of technology costs is impossible 23 Copyright Jonathan G. Koomey 2016
- 24. Two senses of the word “impossible”: Practically and Theoretically Either way, the net result is the same: inaccurate forecasts 24 Copyright Jonathan G. Koomey 2016
- 25. So what does this result imply for predictions of the costs of energy technologies? 25 Copyright Jonathan G. Koomey 2016
- 26. Some lessons • The world is evolutionary and path dependent – Increasing returns, transaction costs, information asymmetries, bounded rationality, prospect theory – Our actions now affect our options later (so do surprises!) • Experimentation is the order of the day • Use real data to prove results – For nuclear power, we’re in the “show me” stage. Cost projections are no longer enough • Prefer technologies that – are mass produced vs. site-built – have short lead times vs. longer lead times 26 Copyright Jonathan G. Koomey 2016
- 27. Nuke costs: here we go again? Source: Koomey and Hultman 2007. 27 Copyright Jonathan G. Koomey 2016
- 28. “No battle plan survives contact with the enemy.” –Helmuth von Moltke the elder Copyright Jonathan G. Koomey 2016 28
- 29. More lessons • Use physical and technological constraints to define bounding cases. Examples: – 2 degrees Celsius warming limit implies a carbon budget, which implies a certain rate of implementation of non-fossil energy sources to avoid worst effects of climate change. – Certain technologies use materials that are in limited supply. Working backwards from a goal can help identify resource constraints. – Lifetime of power generation technologies and buildings limits penetration of new technologies unless we scrap existing capital 29 Copyright Jonathan G. Koomey 2016
- 30. Reconsidering benefit-cost analysis for climate • "A corollary is that it is fruitless to attempt to determine the "optimal" carbon tax. If neither the costs nor the benefits can be known with any precision, just about the only thing that can be said with certainty about the welfare maximizing price of carbon emissions is that it is greater than zero. Economists have a great deal to say about how to implement such a tax efficiently and effectively, about the similarities and differences between a tax and a system of tradable carbon emissions permits, about about the best way to recycle the revenue from such a tax or permit system. And, as we have seen above, the distributional consequences of such a tax or permit auction plan will affect other economic variables through system-wide feedbacks. However, any attempt to specify the exact level of the "optimal" tax is less an exercise in scientific calculation than a manifestation of the analyst’s willingness to step beyond the limits of established economic knowledge." • –DeCanio, Stephen J. 2003. Economic Models of Climate Change: A Critique. Basingstoke, UK: Palgrave-Macmillan. p.157. Copyright Jonathan G. Koomey 2016 30
- 31. Conclusions • It is impossible to accurately forecast energy technology characteristics because of – structural inconstancy and – pivotal events • Forecasting community has yet to absorb the implications of this insight • To cope we need new ways to think about the future – Experimental approach to implementation (try many things, fail fast, learn quickly, try again) – Rely on physical and technological constraints to create bounding cases – Embrace path dependence (there is no optimal solution, just lots of possible pathways of roughly similar costs) 31 Copyright Jonathan G. Koomey 2016
- 32. “The best way to predict the future is to invent it.” –Alan Kay Copyright Jonathan G. Koomey 2016 32
- 33. Some Key References • Craig, Paul, Ashok Gadgil, and Jonathan Koomey. 2002. "What Can History Teach Us?: A Retrospective Analysis of Long-term Energy Forecasts for the U.S." In Annual Review of Energy and the Environment 2002. Edited by R. H. Socolow, D. Anderson and J. Harte. Palo Alto, CA: Annual Reviews, Inc. pp. 83-118. • Ghanadan, Rebecca, and Jonathan Koomey. 2005. "Using Energy Scenarios to Explore Alternative Energy Pathways in California." Energy Policy. vol. 33, no. 9. June. pp. 1117-1142. • Hirsh, Richard F., and Jonathan G. Koomey. 2015. "Electricity Consumption and Economic Growth: A New Relationship with Significant Consequences?" The Electricity Journal. vol. 28, no. 9. November. pp. 72-84. [http://www.sciencedirect.com/science/article/pii/S1040619015002067] • Koomey, Jonathan. 2008. Turning Numbers into Knowledge: Mastering the Art of Problem Solving. Oakland, CA: Analytics Press. 2nd edition. <http://www.analyticspress.com> • Koomey, Jonathan. 2002. "From My Perspective: Avoiding "The Big Mistake" in Forecasting Technology Adoption." Technological Forecasting and Social Change. vol. 69, no. 5. June. pp. 511-518. • Koomey, Jonathan G., Paul Craig, Ashok Gadgil, and David Lorenzetti. 2003. "Improving long-range energy modeling: A plea for historical retrospectives." The Energy Journal (also LBNL-52448). vol. 24, no. 4. October. pp. 75-92. • Chapter 4: “Why we can’t accurately forecast the future”, in Koomey, Jonathan G. 2012. Cold Cash, Cool Climate: Science-Based Advice for Ecological Entrepreneurs. Burlingame, CA: Analytics Press. [http://www.analyticspress.com/cccc.html] • Koomey, Jonathan. 2013. "Moving Beyond Benefit-Cost Analysis of Climate Change." Environmental Research Letters. vol. 8, no. 041005. December 2. [http://iopscience.iop.org/ 1748-9326/8/4/041005/] • Laitner, J.A., S.J. DeCanio, J.G. Koomey, A.H. Sanstad. (2003) “Room for Improvement: Increasing the Value of Energy Modeling for Policy Analysis.” Utilities Policy, vol. 11, no. 2. June. pp. 87-94. • Scher, Irene, and Jonathan G. Koomey. 2011. "Is Accurate Forecasting of Economic Systems Possible?" Climatic Change. Vol 104, No. 3-4, pp.473-479. 33 Copyright Jonathan G. Koomey 2016
- 34. More Key References • Armstrong, J. Scott, ed. 2001. Principles of Forecasting: A Handbook for Researchers and Practitioners. Norwell, MA: Kluwer Academic Publishers. • Ascher, William. 1978. Forecasting: An Appraisal for Policy Makers and Planners. Baltimore, MD: Johns Hopkins University Press. • Cohn, Steve. 1991. "Paradigm Debates in Nuclear Cost Forecasting." Technological Forecasting and Social Change. vol. 40, no. 2. September. pp. 103-130. • Grubler, Arnulf, Nebojsa Nakicenovic, and David G. Victor. 1999. "Dynamics of energy technologies and global change." Energy Policy. vol. 27, no. 5. May. pp. 247-280. • Hodges, James S., and James A. Dewar. 1992. Is it you or your model talking? A framework for model validation. Santa Monica, CA: RAND. ISBN 0-8330-1223-1. • Huntington, Hillard G. 1994. "Oil Price Forecasting in the 1980s: What Went Wrong?" The Energy Journal. vol. 15, no. 2. pp. 1-22. • Huss, William R. 1985. "Can Electric Utilities Improve Their Forecast Accuracy? The Historical Perspective." In Public Utilities Fortnightly. December 26, 1985. pp. 3-8. • Landsberg, Hans H. 1985. "Energy in Transition: A View from 1960." The Energy Journal. vol. 6, pp. 1-18. • O'Neill, Brian C., and Mausami Desai. 2005. "Accuracy of past projections of U.S. energy consumption." Energy Policy. vol. 33, no. 8. May. pp. 979-993. • Tetlock, Philip E. 2005. Expert Political Judgment: How Good Is It? How Can We Know? Princeton, NJ: Princeton University Press. • Tybout, Richard A. 1957. "The Economics of Nuclear Power." American Economic Review. vol. 47, no. 2. May. pp. 351-360. 34 Copyright Jonathan G. Koomey 2016